Elements of information theory
Elements of information theory
The power of amnesia: learning probabilistic automata with variable memory length
Machine Learning - Special issue on COLT '94
Theory and algorithms for hidden Markov models and generalized hidden Markov models
Theory and algorithms for hidden Markov models and generalized hidden Markov models
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Elements of the Theory of Computation
Elements of the Theory of Computation
Causal architecture, complexity and self-organization in time series and cellular automata
Causal architecture, complexity and self-organization in time series and cellular automata
Predictive state representations: a new theory for modeling dynamical systems
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Observable Operator Models for Discrete Stochastic Time Series
Neural Computation
The context-tree weighting method: basic properties
IEEE Transactions on Information Theory
Looping suffix tree-based inference of partially observable hidden state
ICML '06 Proceedings of the 23rd international conference on Machine learning
Computing in Science and Engineering
Self-Organizing Networked Systems for Technical Applications: A Discussion on Open Issues
IWSOS '08 Proceedings of the 3rd International Workshop on Self-Organizing Systems
A Method to Derive Local Interaction Strategies for Improving Cooperation in Self-Organizing Systems
IWSOS '08 Proceedings of the 3rd International Workshop on Self-Organizing Systems
Reconstruction Failures: Questioning Level Design
Epistemological Aspects of Computer Simulation in the Social Sciences
A Survey of Models and Design Methods for Self-organizing Networked Systems
IWSOS '09 Proceedings of the 4th IFIP TC 6 International Workshop on Self-Organizing Systems
The Degree of Global-State Awareness in Self-Organizing Systems
IWSOS '09 Proceedings of the 4th IFIP TC 6 International Workshop on Self-Organizing Systems
The computational structure of spike trains
Neural Computation
Discovering functional communities in dynamical networks
ICML'06 Proceedings of the 2006 conference on Statistical network analysis
KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
Vector space formulation of probabilistic finite state automata
Journal of Computer and System Sciences
Learning to make predictions in partially observable environments without a generative model
Journal of Artificial Intelligence Research
Adaptive pattern classification for symbolic dynamic systems
Signal Processing
Picking up the pieces: Causal states in noisy data, and how to recover them
Pattern Recognition Letters
Encoding through patterns: Regression tree-based neuronal population models
Neural Computation
Data Mining and Knowledge Discovery
| Hi-index | 0.00 |
We present a new method for nonlinear prediction of discrete random sequences under minimal structural assumptions. We give a mathematical construction for optimal predictors of such processes, in the form of hidden Markov models. We then describe an algorithm, CSSR (Causal-State Splitting Reconstruction), which approximates the ideal predictor from data. We discuss the reliability of CSSR, its data requirements, and its performance in simulations. Finally, we compare our approach to existing methods using variable-length Markov models and cross-validated hidden Markov models, and show theoretically and experimentally that our method delivers results superior to the former and at least comparable to the latter.